Stochastic differential equations (3rd ed.): an introduction with applications
Stochastic differential equations (3rd ed.): an introduction with applications
Prediction Markets as Decision Support Systems
Information Systems Frontiers
Modeling a Presidential Prediction Market
Management Science
Modeling information incorporation in markets, with application to detecting and explaining events
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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There is significant experimental evidence that prediction markets are efficient mechanisms for aggregating information and are more accurate in forecasting events than traditional forecasting methods, such as polls. Interpretation of prediction market prices as probabilities has been extensively studied in the literature. However there is little research on the volatility of prediction market prices. Given that volatility is fundamental in estimating significance of price movements, it is important to have a better understanding of the volatility of the contract prices. This paper presents a model of a prediction market with binary payoff on a competitive event involving two parties. In our model, each party has a latent underlying "ability" process that describes its ability to win and evolves as an Ito diffusion. We show that, if the prediction market for this event is efficient and unbiased, the price of the corresponding contract also follows a diffusion and its instantaneous volatility is a function of the current contract price and its time to expiration. In the experimental section, we validate our model on a set of InTrade prediction markets and show that our model is consistent with the observed volatility of contract returns. Our model also outperforms existing volatility models in predicting future contract volatility from historical price data. To demonstrate the practical value of our model, we apply it to pricing options on prediction market contracts, such as those recently introduced by InTrade. Other potential applications of this model include detection of significant market moves and improving forecast standard errors.